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Record W3133773825 · doi:10.3390/jrfm14030116

Towards a New Form of Undemocratic Capitalism: Introducing Macro-Equity to Finance Development Post COVID-19 Crisis

2021· article· en· W3133773825 on OpenAlexvenueno aff
Arvind Ashta

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSustainable Finance and Green Bonds
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInjusticeEquity (law)Just societyDividendFinancePublic economicsPoliticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Sustainable Development Goal 16 talks about Peace, Justice, and Strong Institutions, and goal 10 talks about reducing inequality. A major problem exposed by the COVID-19 crisis is that public deficits seem to be the normal state in the business cycle’s booms and downturns, limiting capacity for emergencies. Corporate capitalism has an incentive to perpetuate deficits to increase growth, provide risk-free interest income to financial institutions, and to increase inequalities and economic injustice. To counter this problem, the purpose of this communication is to suggest that countries need to issue equity capital, which we term macro-equity. This macro-equity will give dividends to its shareholders in times of public surplus and issue new shares in times of public deficits. The communication is written as a mind experiment, debating the issues that may arise. This proposal raises many questions of an ethical and moral nature that will lead to passionate debate. The use of macro-equity will reduce countries’ stress, created by high public debt. With appropriate incentives, it may create an entrepreneurial mindset in political leaders that may even reduce corruption and promote redistribution. The moral and ethical issues need to be weighed against the street violence in the absence of any change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.248
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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